def predictFromArchive(archivPath,
net,
wantedShape=(41, 53, 38, 6),
crop=(slice(4, 28), slice(20, 44), slice(7, 31)),
resizeFactor=2):
tensorFile = extractNiftiFromZipArchive(archivPath)
tensor, name = getArrayFromNifti(tensorFile)
os.remove(tensorFile)
tensor = [tensor]
name = [name]
tensor = resizeTensors(tensor, wantedShape)
tensor = cropBlockResize(tensor, resizeFactor, crop)
tensor = normalizeByIndividualMean(tensor)
tensor = np.stack(tensor)
tensor = torch.from_numpy(tensor).type(torch.float32)
tensor = Variable(tensor).view(-1, dimIn)
net_out = net(tensor)
prediction = net_out.max(1)[1]
predictionStringArrOld = [
"no axis is flipped", "the x axis is flipped", "the y axis is flipped",
"the z axis is flipped", "it has no idea what's happening"
]
predictionStringArrProfessional = [
"you can process the data as is",
"you should flip the x axis in the bvec",
"you should flip the y axis in the bvec",
"you should flip the z axis in the bvec",
"You should check this subject manually"
]
for i, name in enumerate([name]):
predCertainty = F.softmax(net_out[i],
dim=0)[prediction[i]].detach().numpy() * 100
pred = prediction[i]
if predCertainty < 99:
print(
f"{predictionStringArrProfessional[4]} for {name[i]}. ({100-predCertainty}% unsure"
)
else:
print(
f"I am {predCertainty:.3f}% pseudo sure that {predictionStringArrProfessional[pred]} for {name[i]}."
)
print(
f"[Pseudo certainty is at {predCertainty}% for {predictionStringArrOld[pred]}]"
)
from src.models.simple_net import SimpleNet
tensorDir = '/black/localhome/reith/Desktop/projects/Tensors/test/'
networkWeights = 'models/trained_simplenet.torch'
wantedShape = (81, 106, 76, 6)
crop = (slice(7, 55), slice(40, 88), slice(14, 62))
resizeFactor = 4
dimIn = 12 * 12 * 12 * 6
dimOut = 4
net = SimpleNet(dimIn=dimIn, dimOut=dimOut)
net.load_state_dict(torch.load(networkWeights))
tensors, names = getTensorList_general(tensorDir, giveNames=True)
tensors = resizeTensors(tensors, wantedShape)
tensors = cropBlockResize(tensors, resizeFactor, crop)
tensors = normalizeByIndividualMean(tensors)
tensors = np.stack(tensors)
tensors = torch.from_numpy(tensors).type(torch.float32)
tensors = Variable(tensors).view(-1, dimIn)
net_out = net(tensors)
prediction = net_out.max(1)[1]
predictionStringArr = ["no axis", "the x axis", "the y axis", "the z axis"]
for i, name in enumerate(names):
print(
f"The magic blackbox thinks that {predictionStringArr[prediction[i]]} is flipped for {names[i]}."
)
print(
f"[Pseudo certainty is at {F.softmax(net_out[i], dim=0)[prediction[i]].detach().numpy()*100}%]"
)